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A Clinical Prediction Model to Assess Risk for Chemotherapy-Related Hospitalization in Patients Initiating Palliative Chemotherapy.

ABSTRACT: Chemotherapy-related hospitalizations in patients with advanced cancer are common, distressing, and costly. Methods to identify patients at high risk of chemotherapy toxic effects will permit development of targeted strategies to prevent chemotherapy-related hospitalizations.To demonstrate the feasibility of using readily available clinical data to assess patient-specific risk of chemotherapy-related hospitalization.Nested case-control study conducted from January 2003 through December 2011 at the Mass General/North Shore Cancer Center, a community-based cancer center in northeastern Massachusetts. The parent cohort included 1579 consecutive patients with advanced solid-tumor cancer receiving palliative-intent chemotherapy. Case patients (n?=?146) included all patients from the parent cohort who experienced a chemotherapy-related hospitalization. Controls (n?=?292) were randomly selected from 1433 patients who did not experience a chemotherapy-related hospitalization.Putative risk factors for chemotherapy-related hospitalization-including patient characteristics, treatment characteristics, and pretreatment laboratory values-were abstracted from medical records. Multivariable logistic regression was used to model the patient-specific risk of chemotherapy-related hospitalization.Chemotherapy-related hospitalization, as adjudicated by the oncology clinical care team within a systematic quality-assessment program.A total of 146 (9.2%) of 1579 patients from the parent cohort experienced a chemotherapy-related hospitalization. In multivariate regression, 7 variables were significantly associated with chemotherapy-related hospitalization: age, Charlson comorbidity score, creatinine clearance, calcium level, below-normal white blood cell and/or platelet count, polychemotherapy (vs monotherapy), and receipt of camptothecin chemotherapy. The median predicted risk of chemotherapy-related hospitalization was 6.0% (interquartile range [IQR], 3.6%-11.4%) in control patients and 14.7% (IQR, 6.8%-22.5%) in case patients. The bootstrap-adjusted C statistic was 0.71 (95% CI, 0.66-0.75). At a risk threshold of 15%, the model exhibited a sensitivity of 49% (95% CI, 41%-57%) and a specificity of 85% (95% CI, 81%-89%) for predicting chemotherapy-related hospitalization.In patients initiating palliative chemotherapy for cancer, readily available clinical data were associated with the patient-specific risk of chemotherapy-related hospitalization. External validation and evaluation in the context of a clinical decision support tool are warranted.

SUBMITTER: Brooks GA

PROVIDER: S-EPMC4576342 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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A Clinical Prediction Model to Assess Risk for Chemotherapy-Related Hospitalization in Patients Initiating Palliative Chemotherapy.

Brooks Gabriel A GA Kansagra Ankit J AJ Rao Sowmya R SR Weitzman James I JI Linden Erica A EA Jacobson Joseph O JO

JAMA oncology 20150701 4

<h4>Importance</h4>Chemotherapy-related hospitalizations in patients with advanced cancer are common, distressing, and costly. Methods to identify patients at high risk of chemotherapy toxic effects will permit development of targeted strategies to prevent chemotherapy-related hospitalizations.<h4>Objective</h4>To demonstrate the feasibility of using readily available clinical data to assess patient-specific risk of chemotherapy-related hospitalization.<h4>Design, setting, and participants</h4>N ...[more]

PMID: 26181251

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Project description:Background & aimsApproximately 50% of all patients with pancreatic ductal adenocarcinoma (PDA) develop diabetes mellitus before their cancer diagnosis. Screening individuals with new-onset diabetes might allow earlier diagnosis of PDA. We sought to develop and validate a PDA risk prediction model to identify high-risk individuals among those with new-onset diabetes.MethodsWe conducted a retrospective cohort study in a population representative database from the United Kingdom. Individuals with incident diabetes after the age of 35 years and 3 or more years of follow-up after diagnosis of diabetes were eligible for inclusion. Candidate predictors consisted of epidemiologic and clinical characteristics available at the time of diabetes diagnosis. Variables with P values <.25 in the univariable analyses were evaluated using backward stepwise approach. Model discrimination was assessed using receiver operating characteristic curve analysis. Calibration was evaluated using the Hosmer-Lemeshow test. Results were internally validated using a bootstrapping procedure.ResultsWe analyzed data from 109,385 patients with new-onset diabetes. Among them, 390 (0.4%) were diagnosed with PDA within 3 years. The final model (area under the curve, 0.82; 95% confidence interval, 0.75-0.89) included age, body mass index, change in body mass index, smoking, use of proton pump inhibitors, and anti-diabetic medications, as well as levels of hemoglobin A1C, cholesterol, hemoglobin, creatinine, and alkaline phosphatase. Bootstrapping validation showed negligible optimism. If the predicted risk threshold for definitive PDA screening was set at 1% over 3 years, only 6.19% of the new-onset diabetes population would undergo definitive screening, which would identify patients with PDA with 44.7% sensitivity, 94.0% specificity, and a positive predictive value of 2.6%.ConclusionsWe developed a risk model based on widely available clinical parameters to help identify patients with new-onset diabetes who might benefit from PDA screening.

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A Clinical Prediction Model to Assess Risk for Chemotherapy-Related Hospitalization in Patients Initiating Palliative Chemotherapy.

Publications

A Clinical Prediction Model to Assess Risk for Chemotherapy-Related Hospitalization in Patients Initiating Palliative Chemotherapy.

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